Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations560426
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory319.5 MiB
Average record size in memory597.7 B

Variable types

Numeric17
Boolean1
Categorical5
Text1
DateTime1

Alerts

player_to_predict has constant value "True" Constant
a is highly overall correlated with sHigh correlation
absolute_yardline_number is highly overall correlated with ball_land_x and 2 other fieldsHigh correlation
ball_land_x is highly overall correlated with absolute_yardline_number and 2 other fieldsHigh correlation
ball_land_y is highly overall correlated with y_in and 1 other fieldsHigh correlation
dir is highly overall correlated with play_directionHigh correlation
frame_id is highly overall correlated with sHigh correlation
play_direction is highly overall correlated with dirHigh correlation
player_position is highly overall correlated with player_role and 1 other fieldsHigh correlation
player_role is highly overall correlated with player_position and 1 other fieldsHigh correlation
player_side is highly overall correlated with player_position and 1 other fieldsHigh correlation
s is highly overall correlated with a and 1 other fieldsHigh correlation
x_in is highly overall correlated with absolute_yardline_number and 2 other fieldsHigh correlation
x_out is highly overall correlated with absolute_yardline_number and 2 other fieldsHigh correlation
y_in is highly overall correlated with ball_land_y and 1 other fieldsHigh correlation
y_out is highly overall correlated with ball_land_y and 1 other fieldsHigh correlation
s has 26709 (4.8%) zeros Zeros
a has 23807 (4.2%) zeros Zeros

Reproduction

Analysis started2025-10-13 01:12:45.467912
Analysis finished2025-10-13 01:14:27.031523
Duration1 minute and 41.56 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

game_id
Real number (ℝ)

Distinct272
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0231558 × 109
Minimum2.0230907 × 109
Maximum2.0240107 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-10-13T01:14:27.208598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0230907 × 109
5-th percentile2.023091 × 109
Q12.0231008 × 109
median2.0231112 × 109
Q32.023121 × 109
95-th percentile2.0240106 × 109
Maximum2.0240107 × 109
Range920013
Interquartile range (IQR)20206

Descriptive statistics

Standard deviation202245.29
Coefficient of variation (CV)9.9965257 × 10-5
Kurtosis13.879846
Mean2.0231558 × 109
Median Absolute Deviation (MAD)10392
Skewness3.9777989
Sum1.1338291 × 1015
Variance4.0903156 × 1010
MonotonicityIncreasing
2025-10-13T01:14:27.423497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2023121001 3256
 
0.6%
2023120308 3255
 
0.6%
2023122404 3232
 
0.6%
2023092403 3154
 
0.6%
2023122100 3121
 
0.6%
2023122405 3085
 
0.6%
2023110506 3064
 
0.5%
2023111906 3040
 
0.5%
2023091011 3000
 
0.5%
2023121600 2989
 
0.5%
Other values (262) 529230
94.4%
ValueCountFrequency (%)
2023090700 2349
0.4%
2023091000 1264
0.2%
2023091001 1540
0.3%
2023091002 1851
0.3%
2023091003 1800
0.3%
2023091004 2167
0.4%
2023091005 2187
0.4%
2023091006 2247
0.4%
2023091007 1420
0.3%
2023091008 1766
0.3%
ValueCountFrequency (%)
2024010713 2366
0.4%
2024010712 1490
0.3%
2024010711 2513
0.4%
2024010710 1615
0.3%
2024010709 1842
0.3%
2024010708 2128
0.4%
2024010707 1995
0.4%
2024010706 1527
0.3%
2024010705 1214
0.2%
2024010704 2110
0.4%

play_id
Real number (ℝ)

Distinct4317
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2218.3695
Minimum54
Maximum5258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-10-13T01:14:27.640206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile259
Q11183
median2204
Q33279
95-th percentile4148
Maximum5258
Range5204
Interquartile range (IQR)2096

Descriptive statistics

Standard deviation1246.7481
Coefficient of variation (CV)0.562011
Kurtosis-1.1074145
Mean2218.3695
Median Absolute Deviation (MAD)1051
Skewness0.021674572
Sum1.2432319 × 109
Variance1554380.7
MonotonicityNot monotonic
2025-10-13T01:14:27.855486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 1457
 
0.3%
102 715
 
0.1%
3529 609
 
0.1%
3976 570
 
0.1%
1813 559
 
0.1%
823 543
 
0.1%
1215 534
 
0.1%
3330 524
 
0.1%
3518 522
 
0.1%
56 516
 
0.1%
Other values (4307) 553877
98.8%
ValueCountFrequency (%)
54 21
 
< 0.1%
55 1457
0.3%
56 516
 
0.1%
58 21
 
< 0.1%
59 18
 
< 0.1%
60 24
 
< 0.1%
62 420
 
0.1%
63 30
 
< 0.1%
64 61
 
< 0.1%
67 21
 
< 0.1%
ValueCountFrequency (%)
5258 30
 
< 0.1%
5235 24
 
< 0.1%
5186 40
 
< 0.1%
5183 90
< 0.1%
5170 40
 
< 0.1%
5160 63
< 0.1%
5147 20
 
< 0.1%
5135 110
< 0.1%
5117 20
 
< 0.1%
5057 40
 
< 0.1%

player_to_predict
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size547.4 KiB
True
560426 
ValueCountFrequency (%)
True 560426
100.0%
2025-10-13T01:14:28.013988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

nfl_id
Real number (ℝ)

Distinct1178
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49649.474
Minimum30842
Maximum56673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-10-13T01:14:28.175962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30842
5-th percentile41233
Q145395
median52423
Q354496
95-th percentile55965
Maximum56673
Range25831
Interquartile range (IQR)9101

Descriptive statistics

Standard deviation5087.6921
Coefficient of variation (CV)0.10247223
Kurtosis-1.074336
Mean49649.474
Median Absolute Deviation (MAD)3829
Skewness-0.38867183
Sum2.7824856 × 1010
Variance25884611
MonotonicityNot monotonic
2025-10-13T01:14:28.385671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53533 2241
 
0.4%
44906 2208
 
0.4%
53503 2166
 
0.4%
43351 2148
 
0.4%
44878 2133
 
0.4%
40017 2132
 
0.4%
53554 2098
 
0.4%
52607 2040
 
0.4%
52425 1938
 
0.3%
54514 1933
 
0.3%
Other values (1168) 539389
96.2%
ValueCountFrequency (%)
30842 26
 
< 0.1%
33131 24
 
< 0.1%
35452 16
 
< 0.1%
35459 676
 
0.1%
35470 19
 
< 0.1%
35534 65
 
< 0.1%
37075 41
 
< 0.1%
37078 1801
0.3%
37079 147
 
< 0.1%
37097 45
 
< 0.1%
ValueCountFrequency (%)
56673 689
0.1%
56670 54
 
< 0.1%
56663 5
 
< 0.1%
56613 25
 
< 0.1%
56598 141
 
< 0.1%
56586 45
 
< 0.1%
56582 16
 
< 0.1%
56576 7
 
< 0.1%
56573 157
 
< 0.1%
56547 29
 
< 0.1%

frame_id
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7491819
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2025-10-13T01:14:28.583360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile19
Maximum40
Range39
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.5465102
Coefficient of variation (CV)0.71575429
Kurtosis1.3400018
Mean7.7491819
Median Absolute Deviation (MAD)3
Skewness1.1706547
Sum4342843
Variance30.763775
MonotonicityNot monotonic
2025-10-13T01:14:28.788018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 46045
 
8.2%
2 46045
 
8.2%
3 46045
 
8.2%
4 46045
 
8.2%
5 46045
 
8.2%
6 45791
 
8.2%
7 44042
 
7.9%
8 39442
 
7.0%
9 33662
 
6.0%
10 28223
 
5.0%
Other values (30) 139041
24.8%
ValueCountFrequency (%)
1 46045
8.2%
2 46045
8.2%
3 46045
8.2%
4 46045
8.2%
5 46045
8.2%
6 45791
8.2%
7 44042
7.9%
8 39442
7.0%
9 33662
6.0%
10 28223
5.0%
ValueCountFrequency (%)
40 7
 
< 0.1%
39 7
 
< 0.1%
38 7
 
< 0.1%
37 7
 
< 0.1%
36 11
 
< 0.1%
35 11
 
< 0.1%
34 24
 
< 0.1%
33 51
 
< 0.1%
32 98
< 0.1%
31 233
< 0.1%

play_direction
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.9 MiB
right
281728 
left
278698 

Length

Max length5
Median length5
Mean length4.5027033
Min length4

Characters and Unicode

Total characters2523432
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowright
4th rowright
5th rowright

Common Values

ValueCountFrequency (%)
right 281728
50.3%
left 278698
49.7%

Length

2025-10-13T01:14:28.986882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T01:14:29.130354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
right 281728
50.3%
left 278698
49.7%

Most occurring characters

ValueCountFrequency (%)
t 560426
22.2%
r 281728
11.2%
i 281728
11.2%
g 281728
11.2%
h 281728
11.2%
l 278698
11.0%
e 278698
11.0%
f 278698
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2523432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 560426
22.2%
r 281728
11.2%
i 281728
11.2%
g 281728
11.2%
h 281728
11.2%
l 278698
11.0%
e 278698
11.0%
f 278698
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2523432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 560426
22.2%
r 281728
11.2%
i 281728
11.2%
g 281728
11.2%
h 281728
11.2%
l 278698
11.0%
e 278698
11.0%
f 278698
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2523432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 560426
22.2%
r 281728
11.2%
i 281728
11.2%
g 281728
11.2%
h 281728
11.2%
l 278698
11.0%
e 278698
11.0%
f 278698
11.0%

absolute_yardline_number
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.395003
Minimum11
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:29.306092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile24
Q141
median60
Q379
95-th percentile98
Maximum109
Range98
Interquartile range (IQR)38

Descriptive statistics

Standard deviation23.094026
Coefficient of variation (CV)0.38238306
Kurtosis-0.9051932
Mean60.395003
Median Absolute Deviation (MAD)19
Skewness0.016466436
Sum33846930
Variance533.33405
MonotonicityNot monotonic
2025-10-13T01:14:29.520905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 23308
 
4.2%
35 23252
 
4.1%
65 9627
 
1.7%
55 9217
 
1.6%
50 9016
 
1.6%
46 8907
 
1.6%
51 8082
 
1.4%
71 8062
 
1.4%
61 8028
 
1.4%
74 8012
 
1.4%
Other values (89) 444915
79.4%
ValueCountFrequency (%)
11 1177
0.2%
12 1454
0.3%
13 2263
0.4%
14 1589
0.3%
15 2079
0.4%
16 1751
0.3%
17 2195
0.4%
18 2497
0.4%
19 2296
0.4%
20 2527
0.5%
ValueCountFrequency (%)
109 1835
0.3%
108 1621
0.3%
107 1812
0.3%
106 1812
0.3%
105 2461
0.4%
104 2613
0.5%
103 2199
0.4%
102 3316
0.6%
101 3034
0.5%
100 3004
0.5%
Distinct1177
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size37.5 MiB
2025-10-13T01:14:29.923194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length13.147698
Min length8

Characters and Unicode

Total characters7368312
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJustin Reid
2nd rowJustin Reid
3rd rowJustin Reid
4th rowJustin Reid
5th rowJustin Reid
ValueCountFrequency (%)
jr 13274
 
1.2%
jordan 9201
 
0.8%
brown 9011
 
0.8%
ii 8160
 
0.7%
johnson 8117
 
0.7%
jones 8072
 
0.7%
michael 7847
 
0.7%
wilson 7638
 
0.7%
jackson 7538
 
0.7%
christian 7505
 
0.7%
Other values (1471) 1064749
92.5%
2025-10-13T01:14:30.549418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 655567
 
8.9%
590686
 
8.0%
a 588552
 
8.0%
n 578182
 
7.8%
r 482700
 
6.6%
o 476861
 
6.5%
i 400057
 
5.4%
l 355160
 
4.8%
s 295861
 
4.0%
t 236351
 
3.2%
Other values (47) 2708335
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7368312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 655567
 
8.9%
590686
 
8.0%
a 588552
 
8.0%
n 578182
 
7.8%
r 482700
 
6.6%
o 476861
 
6.5%
i 400057
 
5.4%
l 355160
 
4.8%
s 295861
 
4.0%
t 236351
 
3.2%
Other values (47) 2708335
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7368312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 655567
 
8.9%
590686
 
8.0%
a 588552
 
8.0%
n 578182
 
7.8%
r 482700
 
6.6%
o 476861
 
6.5%
i 400057
 
5.4%
l 355160
 
4.8%
s 295861
 
4.0%
t 236351
 
3.2%
Other values (47) 2708335
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7368312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 655567
 
8.9%
590686
 
8.0%
a 588552
 
8.0%
n 578182
 
7.8%
r 482700
 
6.6%
o 476861
 
6.5%
i 400057
 
5.4%
l 355160
 
4.8%
s 295861
 
4.0%
t 236351
 
3.2%
Other values (47) 2708335
36.8%

player_height
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.2 MiB
6-1
113139 
6-0
111477 
6-2
82225 
5-11
70054 
6-3
46692 
Other values (11)
136839 

Length

Max length4
Median length3
Mean length3.2046515
Min length3

Characters and Unicode

Total characters1795970
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6-1
2nd row6-1
3rd row6-1
4th row6-1
5th row6-1

Common Values

ValueCountFrequency (%)
6-1 113139
20.2%
6-0 111477
19.9%
6-2 82225
14.7%
5-11 70054
12.5%
6-3 46692
8.3%
5-10 44638
 
8.0%
6-4 33541
 
6.0%
5-9 24842
 
4.4%
6-5 19724
 
3.5%
6-6 7061
 
1.3%
Other values (6) 7033
 
1.3%

Length

2025-10-13T01:14:30.772588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6-1 113139
20.2%
6-0 111477
19.9%
6-2 82225
14.7%
5-11 70054
12.5%
6-3 46692
8.3%
5-10 44638
 
8.0%
6-4 33541
 
6.0%
5-9 24842
 
4.4%
6-5 19724
 
3.5%
6-6 7061
 
1.3%
Other values (6) 7033
 
1.3%

Most occurring characters

ValueCountFrequency (%)
- 560426
31.2%
6 422527
23.5%
1 297885
16.6%
5 164993
 
9.2%
0 156115
 
8.7%
2 82225
 
4.6%
3 46692
 
2.6%
4 33541
 
1.9%
9 24852
 
1.4%
8 5187
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1795970
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 560426
31.2%
6 422527
23.5%
1 297885
16.6%
5 164993
 
9.2%
0 156115
 
8.7%
2 82225
 
4.6%
3 46692
 
2.6%
4 33541
 
1.9%
9 24852
 
1.4%
8 5187
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1795970
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 560426
31.2%
6 422527
23.5%
1 297885
16.6%
5 164993
 
9.2%
0 156115
 
8.7%
2 82225
 
4.6%
3 46692
 
2.6%
4 33541
 
1.9%
9 24852
 
1.4%
8 5187
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1795970
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 560426
31.2%
6 422527
23.5%
1 297885
16.6%
5 164993
 
9.2%
0 156115
 
8.7%
2 82225
 
4.6%
3 46692
 
2.6%
4 33541
 
1.9%
9 24852
 
1.4%
8 5187
 
0.3%

player_weight
Real number (ℝ)

Distinct143
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.45975
Minimum153
Maximum358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:30.987840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum153
5-th percentile180
Q1193
median203
Q3220
95-th percentile250
Maximum358
Range205
Interquartile range (IQR)27

Descriptive statistics

Standard deviation21.723023
Coefficient of variation (CV)0.10420728
Kurtosis0.95490164
Mean208.45975
Median Absolute Deviation (MAD)12
Skewness0.8695942
Sum1.1682626 × 108
Variance471.88973
MonotonicityNot monotonic
2025-10-13T01:14:31.212955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 38725
 
6.9%
190 33277
 
5.9%
195 32923
 
5.9%
205 28483
 
5.1%
210 19881
 
3.5%
202 17029
 
3.0%
215 15497
 
2.8%
185 13561
 
2.4%
250 13484
 
2.4%
193 12963
 
2.3%
Other values (133) 334603
59.7%
ValueCountFrequency (%)
153 935
 
0.2%
155 210
 
< 0.1%
162 319
 
0.1%
165 2188
0.4%
170 5297
0.9%
172 1537
 
0.3%
173 468
 
0.1%
174 165
 
< 0.1%
175 4291
0.8%
176 41
 
< 0.1%
ValueCountFrequency (%)
358 9
 
< 0.1%
347 32
< 0.1%
342 10
 
< 0.1%
340 20
< 0.1%
338 7
 
< 0.1%
336 17
< 0.1%
330 15
< 0.1%
329 15
< 0.1%
328 7
 
< 0.1%
325 10
 
< 0.1%
Distinct980
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.3 MiB
Minimum1984-05-19 00:00:00
Maximum2002-10-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-13T01:14:31.411433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:31.618324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

player_position
Categorical

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.6 MiB
CB
171798 
WR
107561 
FS
71426 
SS
56589 
ILB
39868 
Other values (12)
113184 

Length

Max length3
Median length2
Mean length2.1666625
Min length1

Characters and Unicode

Total characters1214254
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSS
2nd rowSS
3rd rowSS
4th rowSS
5th rowSS

Common Values

ValueCountFrequency (%)
CB 171798
30.7%
WR 107561
19.2%
FS 71426
12.7%
SS 56589
 
10.1%
ILB 39868
 
7.1%
TE 32190
 
5.7%
OLB 28199
 
5.0%
MLB 27619
 
4.9%
RB 18674
 
3.3%
DE 2468
 
0.4%
Other values (7) 4034
 
0.7%

Length

2025-10-13T01:14:31.816912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cb 171798
30.7%
wr 107561
19.2%
fs 71426
12.7%
ss 56589
 
10.1%
ilb 39868
 
7.1%
te 32190
 
5.7%
olb 28199
 
5.0%
mlb 27619
 
4.9%
rb 18674
 
3.3%
de 2468
 
0.4%
Other values (7) 4034
 
0.7%

Most occurring characters

ValueCountFrequency (%)
B 287287
23.7%
S 186871
15.4%
C 171798
14.1%
R 126235
10.4%
W 107561
 
8.9%
L 95705
 
7.9%
F 72186
 
5.9%
I 39868
 
3.3%
E 34658
 
2.9%
T 32828
 
2.7%
Other values (5) 59257
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1214254
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 287287
23.7%
S 186871
15.4%
C 171798
14.1%
R 126235
10.4%
W 107561
 
8.9%
L 95705
 
7.9%
F 72186
 
5.9%
I 39868
 
3.3%
E 34658
 
2.9%
T 32828
 
2.7%
Other values (5) 59257
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1214254
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 287287
23.7%
S 186871
15.4%
C 171798
14.1%
R 126235
10.4%
W 107561
 
8.9%
L 95705
 
7.9%
F 72186
 
5.9%
I 39868
 
3.3%
E 34658
 
2.9%
T 32828
 
2.7%
Other values (5) 59257
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1214254
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 287287
23.7%
S 186871
15.4%
C 171798
14.1%
R 126235
10.4%
W 107561
 
8.9%
L 95705
 
7.9%
F 72186
 
5.9%
I 39868
 
3.3%
E 34658
 
2.9%
T 32828
 
2.7%
Other values (5) 59257
 
4.9%

player_side
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.2 MiB
Defense
400584 
Offense
159842 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3922982
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDefense
2nd rowDefense
3rd rowDefense
4th rowDefense
5th rowDefense

Common Values

ValueCountFrequency (%)
Defense 400584
71.5%
Offense 159842
 
28.5%

Length

2025-10-13T01:14:31.995404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T01:14:32.132030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
defense 400584
71.5%
offense 159842
 
28.5%

Most occurring characters

ValueCountFrequency (%)
e 1521436
38.8%
f 720268
18.4%
n 560426
 
14.3%
s 560426
 
14.3%
D 400584
 
10.2%
O 159842
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3922982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1521436
38.8%
f 720268
18.4%
n 560426
 
14.3%
s 560426
 
14.3%
D 400584
 
10.2%
O 159842
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3922982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1521436
38.8%
f 720268
18.4%
n 560426
 
14.3%
s 560426
 
14.3%
D 400584
 
10.2%
O 159842
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3922982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1521436
38.8%
f 720268
18.4%
n 560426
 
14.3%
s 560426
 
14.3%
D 400584
 
10.2%
O 159842
 
4.1%

player_role
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.9 MiB
Defensive Coverage
400584 
Targeted Receiver
159842 

Length

Max length18
Median length18
Mean length17.714785
Min length17

Characters and Unicode

Total characters9927826
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDefensive Coverage
2nd rowDefensive Coverage
3rd rowDefensive Coverage
4th rowDefensive Coverage
5th rowDefensive Coverage

Common Values

ValueCountFrequency (%)
Defensive Coverage 400584
71.5%
Targeted Receiver 159842
 
28.5%

Length

2025-10-13T01:14:32.287679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-13T01:14:32.426083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
defensive 400584
35.7%
coverage 400584
35.7%
targeted 159842
 
14.3%
receiver 159842
 
14.3%

Most occurring characters

ValueCountFrequency (%)
e 2802130
28.2%
v 961010
 
9.7%
r 720268
 
7.3%
g 560426
 
5.6%
a 560426
 
5.6%
i 560426
 
5.6%
560426
 
5.6%
D 400584
 
4.0%
o 400584
 
4.0%
C 400584
 
4.0%
Other values (8) 2000962
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9927826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2802130
28.2%
v 961010
 
9.7%
r 720268
 
7.3%
g 560426
 
5.6%
a 560426
 
5.6%
i 560426
 
5.6%
560426
 
5.6%
D 400584
 
4.0%
o 400584
 
4.0%
C 400584
 
4.0%
Other values (8) 2000962
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9927826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2802130
28.2%
v 961010
 
9.7%
r 720268
 
7.3%
g 560426
 
5.6%
a 560426
 
5.6%
i 560426
 
5.6%
560426
 
5.6%
D 400584
 
4.0%
o 400584
 
4.0%
C 400584
 
4.0%
Other values (8) 2000962
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9927826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2802130
28.2%
v 961010
 
9.7%
r 720268
 
7.3%
g 560426
 
5.6%
a 560426
 
5.6%
i 560426
 
5.6%
560426
 
5.6%
D 400584
 
4.0%
o 400584
 
4.0%
C 400584
 
4.0%
Other values (8) 2000962
20.2%

x_in
Real number (ℝ)

High correlation 

Distinct10915
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.322071
Minimum3.79
Maximum116.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:32.601515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.79
5-th percentile22.03
Q142.51
median60.11
Q377.99
95-th percentile100.03
Maximum116.33
Range112.54
Interquartile range (IQR)35.48

Descriptive statistics

Standard deviation23.305106
Coefficient of variation (CV)0.38634458
Kurtosis-0.66053917
Mean60.322071
Median Absolute Deviation (MAD)17.73
Skewness0.027164123
Sum33806057
Variance543.12795
MonotonicityNot monotonic
2025-10-13T01:14:32.819721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.63 120
 
< 0.1%
54.15 118
 
< 0.1%
50.39 118
 
< 0.1%
64.11 115
 
< 0.1%
49.84 114
 
< 0.1%
49.57 112
 
< 0.1%
39.5 112
 
< 0.1%
48.62 111
 
< 0.1%
69.16 111
 
< 0.1%
78.21 110
 
< 0.1%
Other values (10905) 559285
99.8%
ValueCountFrequency (%)
3.79 1
 
< 0.1%
3.84 1
 
< 0.1%
3.88 3
< 0.1%
3.89 2
< 0.1%
3.9 2
< 0.1%
3.91 1
 
< 0.1%
3.92 2
< 0.1%
3.97 1
 
< 0.1%
4.02 1
 
< 0.1%
4.07 1
 
< 0.1%
ValueCountFrequency (%)
116.33 1
< 0.1%
116.23 1
< 0.1%
116.19 1
< 0.1%
116.14 1
< 0.1%
116.13 1
< 0.1%
116.07 1
< 0.1%
116.04 1
< 0.1%
116 1
< 0.1%
115.94 1
< 0.1%
115.92 1
< 0.1%

y_in
Real number (ℝ)

High correlation 

Distinct4499
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.726465
Minimum1.54
Maximum50.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:33.035018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.54
5-th percentile10.15
Q118.44
median26.62
Q335.07
95-th percentile43.48
Maximum50.45
Range48.91
Interquartile range (IQR)16.63

Descriptive statistics

Standard deviation10.139143
Coefficient of variation (CV)0.37936717
Kurtosis-1.0365308
Mean26.726465
Median Absolute Deviation (MAD)8.32
Skewness0.012206226
Sum14978206
Variance102.80223
MonotonicityNot monotonic
2025-10-13T01:14:33.242332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.88 295
 
0.1%
18.89 289
 
0.1%
18.69 285
 
0.1%
18.67 285
 
0.1%
35.04 281
 
0.1%
35.02 279
 
< 0.1%
18.84 279
 
< 0.1%
17.99 279
 
< 0.1%
35 278
 
< 0.1%
18.56 276
 
< 0.1%
Other values (4489) 557600
99.5%
ValueCountFrequency (%)
1.54 1
< 0.1%
1.57 1
< 0.1%
1.64 1
< 0.1%
1.75 1
< 0.1%
1.89 1
< 0.1%
2.07 1
< 0.1%
2.29 1
< 0.1%
2.54 1
< 0.1%
2.55 1
< 0.1%
2.64 1
< 0.1%
ValueCountFrequency (%)
50.45 1
< 0.1%
50.23 1
< 0.1%
50.22 1
< 0.1%
50.21 1
< 0.1%
50.14 1
< 0.1%
50.13 1
< 0.1%
50.12 1
< 0.1%
50.11 1
< 0.1%
50.1 1
< 0.1%
50.09 1
< 0.1%

s
Real number (ℝ)

High correlation  Zeros 

Distinct960
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7247875
Minimum0
Maximum9.88
Zeros26709
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:33.444755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.31
median1.13
Q32.6
95-th percentile5.54
Maximum9.88
Range9.88
Interquartile range (IQR)2.29

Descriptive statistics

Standard deviation1.7900426
Coefficient of variation (CV)1.0378337
Kurtosis1.4200545
Mean1.7247875
Median Absolute Deviation (MAD)0.96
Skewness1.3522084
Sum966615.76
Variance3.2042525
MonotonicityNot monotonic
2025-10-13T01:14:33.654909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26709
 
4.8%
0.01 12210
 
2.2%
0.02 8192
 
1.5%
0.03 6433
 
1.1%
0.04 5141
 
0.9%
0.05 4505
 
0.8%
0.06 4143
 
0.7%
0.07 3752
 
0.7%
0.08 3525
 
0.6%
0.09 3470
 
0.6%
Other values (950) 482346
86.1%
ValueCountFrequency (%)
0 26709
4.8%
0.01 12210
2.2%
0.02 8192
 
1.5%
0.03 6433
 
1.1%
0.04 5141
 
0.9%
0.05 4505
 
0.8%
0.06 4143
 
0.7%
0.07 3752
 
0.7%
0.08 3525
 
0.6%
0.09 3470
 
0.6%
ValueCountFrequency (%)
9.88 1
< 0.1%
9.85 1
< 0.1%
9.84 1
< 0.1%
9.83 1
< 0.1%
9.82 1
< 0.1%
9.79 1
< 0.1%
9.74 1
< 0.1%
9.69 1
< 0.1%
9.65 1
< 0.1%
9.64 1
< 0.1%

a
Real number (ℝ)

High correlation  Zeros 

Distinct912
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7609783
Minimum0
Maximum16.75
Zeros23807
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:33.851791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.54
median1.43
Q32.69
95-th percentile4.64
Maximum16.75
Range16.75
Interquartile range (IQR)2.15

Descriptive statistics

Standard deviation1.4783726
Coefficient of variation (CV)0.83951779
Kurtosis0.2184793
Mean1.7609783
Median Absolute Deviation (MAD)1.01
Skewness0.8701295
Sum986898.02
Variance2.1855856
MonotonicityNot monotonic
2025-10-13T01:14:34.599256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23807
 
4.2%
0.01 9319
 
1.7%
0.02 5620
 
1.0%
0.03 4619
 
0.8%
0.04 3833
 
0.7%
0.05 3592
 
0.6%
0.06 3342
 
0.6%
0.07 2927
 
0.5%
0.08 2622
 
0.5%
0.09 2484
 
0.4%
Other values (902) 498261
88.9%
ValueCountFrequency (%)
0 23807
4.2%
0.01 9319
 
1.7%
0.02 5620
 
1.0%
0.03 4619
 
0.8%
0.04 3833
 
0.7%
0.05 3592
 
0.6%
0.06 3342
 
0.6%
0.07 2927
 
0.5%
0.08 2622
 
0.5%
0.09 2484
 
0.4%
ValueCountFrequency (%)
16.75 1
< 0.1%
16.06 1
< 0.1%
14.29 1
< 0.1%
12.36 1
< 0.1%
12.27 1
< 0.1%
12.1 1
< 0.1%
11.81 1
< 0.1%
11.65 1
< 0.1%
11.48 1
< 0.1%
11.44 1
< 0.1%

dir
Real number (ℝ)

High correlation 

Distinct36001
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.80208
Minimum0
Maximum360
Zeros27
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:34.817619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.45
Q191.24
median179.5
Q3270.61
95-th percentile335.92
Maximum360
Range360
Interquartile range (IQR)179.37

Descriptive statistics

Standard deviation100.43069
Coefficient of variation (CV)0.5554731
Kurtosis-1.291019
Mean180.80208
Median Absolute Deviation (MAD)89.68
Skewness0.00086804415
Sum1.0132618 × 108
Variance10086.323
MonotonicityNot monotonic
2025-10-13T01:14:35.022882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
268.56 52
 
< 0.1%
87.78 49
 
< 0.1%
269.6 49
 
< 0.1%
85.91 48
 
< 0.1%
94.07 48
 
< 0.1%
88.31 48
 
< 0.1%
90.12 47
 
< 0.1%
91.24 47
 
< 0.1%
90.86 46
 
< 0.1%
93.4 46
 
< 0.1%
Other values (35991) 559946
99.9%
ValueCountFrequency (%)
0 27
< 0.1%
0.01 11
< 0.1%
0.02 15
< 0.1%
0.03 10
 
< 0.1%
0.04 9
 
< 0.1%
0.05 8
 
< 0.1%
0.06 7
 
< 0.1%
0.07 14
< 0.1%
0.08 10
 
< 0.1%
0.09 13
< 0.1%
ValueCountFrequency (%)
360 6
 
< 0.1%
359.99 11
< 0.1%
359.98 13
< 0.1%
359.97 15
< 0.1%
359.96 22
< 0.1%
359.95 14
< 0.1%
359.94 14
< 0.1%
359.93 10
< 0.1%
359.92 9
< 0.1%
359.91 11
< 0.1%

o
Real number (ℝ)

Distinct35497
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.09084
Minimum0
Maximum360
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:35.229360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55.51
Q190.77
median181.91
Q3271.06
95-th percentile307.34
Maximum360
Range360
Interquartile range (IQR)180.29

Descriptive statistics

Standard deviation94.965759
Coefficient of variation (CV)0.52440951
Kurtosis-1.6085351
Mean181.09084
Median Absolute Deviation (MAD)90.14
Skewness-0.00074593717
Sum1.0148801 × 108
Variance9018.4953
MonotonicityNot monotonic
2025-10-13T01:14:35.445946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 301
 
0.1%
266.98 99
 
< 0.1%
90.46 95
 
< 0.1%
94.72 90
 
< 0.1%
91.41 87
 
< 0.1%
94.75 86
 
< 0.1%
95.14 86
 
< 0.1%
269.31 85
 
< 0.1%
278.24 85
 
< 0.1%
85.13 85
 
< 0.1%
Other values (35487) 559327
99.8%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.01 3
 
< 0.1%
0.02 3
 
< 0.1%
0.03 1
 
< 0.1%
0.04 5
< 0.1%
0.05 9
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 4
< 0.1%
0.09 6
< 0.1%
ValueCountFrequency (%)
360 1
 
< 0.1%
359.99 2
 
< 0.1%
359.97 6
< 0.1%
359.96 2
 
< 0.1%
359.95 1
 
< 0.1%
359.94 6
< 0.1%
359.93 1
 
< 0.1%
359.92 5
< 0.1%
359.91 3
< 0.1%
359.9 2
 
< 0.1%

num_frames_output
Real number (ℝ)

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.602608
Minimum5
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:35.641120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q110
median13
Q318
95-th percentile28
Maximum94
Range89
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.6596071
Coefficient of variation (CV)0.45605602
Kurtosis7.687479
Mean14.602608
Median Absolute Deviation (MAD)4
Skewness1.5138066
Sum8183681
Variance44.350367
MonotonicityNot monotonic
2025-10-13T01:14:35.815483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10 51154
 
9.1%
9 48965
 
8.7%
8 46192
 
8.2%
11 43063
 
7.7%
12 38208
 
6.8%
13 34372
 
6.1%
7 32200
 
5.7%
14 29372
 
5.2%
15 25436
 
4.5%
16 23090
 
4.1%
Other values (24) 188374
33.6%
ValueCountFrequency (%)
5 1270
 
0.2%
6 10494
 
1.9%
7 32200
5.7%
8 46192
8.2%
9 48965
8.7%
10 51154
9.1%
11 43063
7.7%
12 38208
6.8%
13 34372
6.1%
14 29372
5.2%
ValueCountFrequency (%)
94 208
 
< 0.1%
55 264
 
< 0.1%
40 280
 
< 0.1%
36 324
 
0.1%
34 820
 
0.1%
33 1342
 
0.2%
32 1710
 
0.3%
31 4302
0.8%
30 3133
 
0.6%
29 8442
1.5%

ball_land_x
Real number (ℝ)

High correlation 

Distinct7491
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.293877
Minimum-5.2600002
Maximum125.85
Zeros0
Zeros (%)0.0%
Negative1074
Negative (%)0.2%
Memory size4.3 MiB
2025-10-13T01:14:36.005219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.2600002
5-th percentile12.19
Q141.900002
median60.189999
Q378.900002
95-th percentile108.84
Maximum125.85
Range131.11
Interquartile range (IQR)37

Descriptive statistics

Standard deviation27.361703
Coefficient of variation (CV)0.45380566
Kurtosis-0.50751456
Mean60.293877
Median Absolute Deviation (MAD)18.509998
Skewness0.0098507749
Sum33790257
Variance748.66278
MonotonicityNot monotonic
2025-10-13T01:14:36.214316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.43000031 423
 
0.1%
49.84999847 406
 
0.1%
66.23999786 401
 
0.1%
45.63000107 396
 
0.1%
73.68000031 395
 
0.1%
69.61000061 395
 
0.1%
46.15000153 389
 
0.1%
38.56999969 385
 
0.1%
62.43000031 382
 
0.1%
50.95000076 376
 
0.1%
Other values (7481) 556478
99.3%
ValueCountFrequency (%)
-5.260000229 64
< 0.1%
-4.429999828 48
< 0.1%
-4.050000191 42
< 0.1%
-3.319999933 88
< 0.1%
-3 51
< 0.1%
-2.720000029 22
 
< 0.1%
-2.079999924 85
< 0.1%
-1.909999967 68
< 0.1%
-1.059999943 104
< 0.1%
-0.8199999928 70
< 0.1%
ValueCountFrequency (%)
125.8499985 64
< 0.1%
124.6600037 42
< 0.1%
124.5400009 70
< 0.1%
124.4300003 64
< 0.1%
123.9599991 100
< 0.1%
123.2900009 104
< 0.1%
123.1299973 64
< 0.1%
122.9700012 75
< 0.1%
122.8000031 42
< 0.1%
122.2600021 51
< 0.1%

ball_land_y
Real number (ℝ)

High correlation 

Distinct5030
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.567883
Minimum-3.9100001
Maximum57.330002
Zeros0
Zeros (%)0.0%
Negative6034
Negative (%)1.1%
Memory size4.3 MiB
2025-10-13T01:14:36.420365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.9100001
5-th percentile2.47
Q111.24
median26.389999
Q341.599998
95-th percentile50.880001
Maximum57.330002
Range61.240002
Interquartile range (IQR)30.359999

Descriptive statistics

Standard deviation16.482688
Coefficient of variation (CV)0.620399
Kurtosis-1.3391932
Mean26.567883
Median Absolute Deviation (MAD)15.19
Skewness0.014804195
Sum14889332
Variance271.679
MonotonicityNot monotonic
2025-10-13T01:14:36.648629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.50999832 561
 
0.1%
49.47999954 544
 
0.1%
18.56999969 466
 
0.1%
51.40999985 465
 
0.1%
1.5 464
 
0.1%
1.809999943 461
 
0.1%
47.54999924 458
 
0.1%
43.58000183 455
 
0.1%
49.54000092 440
 
0.1%
3.75 437
 
0.1%
Other values (5020) 555675
99.2%
ValueCountFrequency (%)
-3.910000086 34
< 0.1%
-3.660000086 38
< 0.1%
-3.440000057 80
< 0.1%
-3.130000114 78
< 0.1%
-3.049999952 54
< 0.1%
-2.960000038 28
 
< 0.1%
-2.890000105 38
< 0.1%
-2.809999943 54
< 0.1%
-2.710000038 80
< 0.1%
-2.690000057 48
< 0.1%
ValueCountFrequency (%)
57.33000183 36
< 0.1%
57.18000031 32
< 0.1%
56.95000076 45
< 0.1%
56.84999847 8
 
< 0.1%
56.41999817 66
< 0.1%
56.00999832 60
< 0.1%
55.93999863 42
< 0.1%
55.88999939 26
 
< 0.1%
55.88000107 45
< 0.1%
55.86999893 58
< 0.1%

x_out
Real number (ℝ)

High correlation 

Distinct11894
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.311604
Minimum0.02
Maximum120.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:36.855745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile16.62
Q143.08
median60.13
Q377.34
95-th percentile105.35
Maximum120.83
Range120.81
Interquartile range (IQR)34.26

Descriptive statistics

Standard deviation25.247203
Coefficient of variation (CV)0.4186127
Kurtosis-0.4652083
Mean60.311604
Median Absolute Deviation (MAD)17.13
Skewness0.026151173
Sum33800191
Variance637.42127
MonotonicityNot monotonic
2025-10-13T01:14:37.067383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.6 117
 
< 0.1%
65.07 110
 
< 0.1%
49.66 110
 
< 0.1%
64.95 109
 
< 0.1%
70.55 109
 
< 0.1%
58.42 109
 
< 0.1%
60.57 108
 
< 0.1%
51.7 108
 
< 0.1%
63.12 108
 
< 0.1%
65.76 107
 
< 0.1%
Other values (11884) 559331
99.8%
ValueCountFrequency (%)
0.02 1
< 0.1%
0.04 1
< 0.1%
0.06 1
< 0.1%
0.1 1
< 0.1%
0.11 1
< 0.1%
0.12 1
< 0.1%
0.16 1
< 0.1%
0.2 1
< 0.1%
0.22 1
< 0.1%
0.24 1
< 0.1%
ValueCountFrequency (%)
120.83 1
< 0.1%
120.57 1
< 0.1%
120.26 1
< 0.1%
119.96 1
< 0.1%
119.82 1
< 0.1%
119.61 1
< 0.1%
119.54 1
< 0.1%
119.52 2
< 0.1%
119.51 1
< 0.1%
119.47 1
< 0.1%

y_out
Real number (ℝ)

High correlation 

Distinct5260
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.604846
Minimum0.33
Maximum53.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.3 MiB
2025-10-13T01:14:37.273319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile6.04
Q114.92
median26.42
Q338.33
95-th percentile47.47
Maximum53.72
Range53.39
Interquartile range (IQR)23.41

Descriptive statistics

Standard deviation13.428138
Coefficient of variation (CV)0.50472525
Kurtosis-1.2047042
Mean26.604846
Median Absolute Deviation (MAD)11.7
Skewness0.02047699
Sum14910048
Variance180.31489
MonotonicityNot monotonic
2025-10-13T01:14:37.488817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.18 181
 
< 0.1%
15.6 179
 
< 0.1%
37.93 176
 
< 0.1%
16.32 174
 
< 0.1%
16.08 174
 
< 0.1%
38.73 174
 
< 0.1%
13.11 173
 
< 0.1%
15.53 173
 
< 0.1%
16.01 173
 
< 0.1%
38.53 173
 
< 0.1%
Other values (5250) 558676
99.7%
ValueCountFrequency (%)
0.33 1
 
< 0.1%
0.36 3
< 0.1%
0.38 1
 
< 0.1%
0.39 1
 
< 0.1%
0.4 1
 
< 0.1%
0.41 1
 
< 0.1%
0.42 6
< 0.1%
0.44 1
 
< 0.1%
0.45 2
 
< 0.1%
0.46 2
 
< 0.1%
ValueCountFrequency (%)
53.72 1
 
< 0.1%
53 3
< 0.1%
52.99 2
< 0.1%
52.98 2
< 0.1%
52.97 3
< 0.1%
52.96 3
< 0.1%
52.95 3
< 0.1%
52.94 1
 
< 0.1%
52.93 4
< 0.1%
52.91 2
< 0.1%

Interactions

2025-10-13T01:14:18.939883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:17.366381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:21.217951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:24.762570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:28.615014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:32.398824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:36.220416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:39.838013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:43.590474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:47.532642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:51.285725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:55.203444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:59.505303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:03.422583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:07.025362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:10.955816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:15.138618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:19.152401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:17.616554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:21.420806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:24.974422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:28.835240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:32.602822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:36.433836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:40.049616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:43.793473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:47.746482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:51.504972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:55.436843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:59.727187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:03.627078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:07.250583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:11.187190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:15.356335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:19.364017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:17.827460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:21.623331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:25.171659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:29.052478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:32.812613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:36.670371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:40.258101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:44.000014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:47.957978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:51.718579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:55.669772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:59.945501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:03.829305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:07.480587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:11.408892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:15.574230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:19.584549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:18.045355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:21.825503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:25.382162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:29.263944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:33.015177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:36.880548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:40.475291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:44.209982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:48.174681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:51.938332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:55.901085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:00.169453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:04.029076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:07.710385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:11.630347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:15.791875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:19.810285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:18.266073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:22.039757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:25.609180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:29.499338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:33.225370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:37.106472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:40.702276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:44.436071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:48.399197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:52.179323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:56.143376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:00.402785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:04.241362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:07.945952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:11.858715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:16.018435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:20.016807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:18.479373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:22.238899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:25.811312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:29.712345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:33.430125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:37.306316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:40.928586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:44.644462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:48.609712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:52.397199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:56.373767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:00.627609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:04.439733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:08.172613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:12.067630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:16.235747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:20.226125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:18.688508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:22.443274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:26.024785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:29.921919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:33.640347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:37.507151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:41.143321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:44.849335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:48.821038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:52.620235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:56.625401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:00.863641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:04.645218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:08.395918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:12.282269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:16.453563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:20.456161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:18.907585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:22.654658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:26.236047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:30.141458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:33.851015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:37.718519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:41.374047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:45.056021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:49.043563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:52.848361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:56.864567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:01.117872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:04.856473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:08.626580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:12.501885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:16.700543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:20.679258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:19.124129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:22.863908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:26.458673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:30.355658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:34.063386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:37.921673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:41.591785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:45.263233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:49.254216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:53.071071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:57.087245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:01.368601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:05.060632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:08.855317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:12.716837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:16.927207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:20.914954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:19.335836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:23.070002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:26.700731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:30.577992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:34.276007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:38.132431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:41.807091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:45.475797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:49.473016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:53.288729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:57.312134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:01.607287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:05.265958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:09.083243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:12.930575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:17.151569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:21.157729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:19.566262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:23.287322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:26.921200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:30.813666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:34.497737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:38.348173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:42.037506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:45.697668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:49.702224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:53.536255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:57.542913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:01.840953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:05.487323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:09.330606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:13.162875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:17.386327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:21.387093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:19.787767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:23.505226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:27.143563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:31.053879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:34.718028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:38.567104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:42.267814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:46.202684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:49.942716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:53.767943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:57.775989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:02.064448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:05.704022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:09.575518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:13.389149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:17.623785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:21.617540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:20.003753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:23.716639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:27.548247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:31.288042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:34.935740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:38.786368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:42.494554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:46.420156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:50.163046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:53.996185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:58.001111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:02.293748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:05.913241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:09.801217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:13.615284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:17.849703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:21.818973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:20.202719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:23.911536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:27.749660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:31.505963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:35.128373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:38.992001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:42.703151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:46.642936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:50.373383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:54.217932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:58.214238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:02.505533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:06.121470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:10.003230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:13.817580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:18.051648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:22.040698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:20.417726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:24.128767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:27.970773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:31.732634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:35.343165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:39.201943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:42.922601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:46.867628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:50.594393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:54.461902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:58.442495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:02.733654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:06.338519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:10.231915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:14.030561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:18.277612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:22.262727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:20.639294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:24.340887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:28.186023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:31.958204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:35.797178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:39.417398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:43.140085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:47.086342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:50.825220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:54.707424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:59.031516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:02.954865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:06.580693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:10.468407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:14.250069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:18.499086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:22.476559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:20.854386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:24.559274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:28.399248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:32.188783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:36.011907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:39.631953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:43.363529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:47.309084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:51.062032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:54.962481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:13:59.269286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:03.198144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:06.804041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:10.709264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:14.477693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-13T01:14:18.722172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-10-13T01:14:37.663076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
aabsolute_yardline_numberball_land_xball_land_ydirframe_idgame_idnfl_idnum_frames_outputoplay_directionplay_idplayer_heightplayer_positionplayer_roleplayer_sideplayer_weightsx_inx_outy_iny_out
a1.0000.003-0.000-0.0040.0000.4920.0100.0050.154-0.0050.006-0.0040.0440.1360.3940.394-0.0150.6430.0020.000-0.003-0.007
absolute_yardline_number0.0031.0000.726-0.0030.093-0.0070.005-0.004-0.014-0.0370.298-0.0100.0180.0190.0060.0060.005-0.0050.9550.799-0.003-0.003
ball_land_x-0.0000.7261.0000.003-0.228-0.008-0.015-0.003-0.0150.1590.451-0.0070.0200.0330.0380.0380.004-0.0100.8290.9520.001-0.001
ball_land_y-0.004-0.0030.0031.000-0.000-0.004-0.0060.011-0.008-0.0000.0330.0050.0290.0700.0340.0340.004-0.005-0.0010.0000.7100.887
dir0.0000.093-0.228-0.0001.0000.0000.013-0.0050.001-0.0290.586-0.0020.0230.1100.1730.1730.0020.0060.003-0.219-0.0010.003
frame_id0.492-0.007-0.008-0.0040.0001.0000.0260.0070.488-0.0010.0120.0300.0240.0530.0560.056-0.0890.712-0.008-0.008-0.004-0.008
game_id0.0100.005-0.015-0.0060.0130.0261.0000.0450.053-0.0070.009-0.0300.0340.0310.0020.0020.0000.022-0.001-0.011-0.002-0.002
nfl_id0.005-0.004-0.0030.011-0.0050.0070.0451.0000.014-0.0070.0180.0180.1410.1380.1080.108-0.150-0.011-0.003-0.0040.0150.013
num_frames_output0.154-0.014-0.015-0.0080.0010.4880.0530.0141.000-0.0070.0310.0620.0540.1110.0870.087-0.1820.392-0.015-0.017-0.006-0.009
o-0.005-0.0370.159-0.000-0.029-0.001-0.007-0.007-0.0071.0000.379-0.0060.0310.0830.1250.1250.012-0.0080.1160.1400.005-0.001
play_direction0.0060.2980.4510.0330.5860.0120.0090.0180.0310.3791.0000.0400.0250.0260.0000.0000.0130.0110.2840.4190.0200.016
play_id-0.004-0.010-0.0070.005-0.0020.030-0.0300.0180.062-0.0060.0401.0000.0200.0220.0110.011-0.0210.016-0.009-0.0100.0040.003
player_height0.0440.0180.0200.0290.0230.0240.0340.1410.0540.0310.0250.0201.0000.3240.3520.3520.3050.0240.0260.0180.0490.026
player_position0.1360.0190.0330.0700.1100.0530.0310.1380.1110.0830.0260.0220.3241.0000.9990.9990.4690.1170.0520.0370.2250.132
player_role0.3940.0060.0380.0340.1730.0560.0020.1080.0870.1250.0000.0110.3520.9991.0001.0000.1980.2800.1250.0320.1400.170
player_side0.3940.0060.0380.0340.1730.0560.0020.1080.0870.1250.0000.0110.3520.9991.0001.0000.1980.2800.1250.0320.1400.170
player_weight-0.0150.0050.0040.0040.002-0.0890.000-0.150-0.1820.0120.013-0.0210.3050.4690.1980.1981.000-0.0890.0040.0030.0060.004
s0.643-0.005-0.010-0.0050.0060.7120.022-0.0110.392-0.0080.0110.0160.0240.1170.2800.280-0.0891.000-0.007-0.009-0.006-0.011
x_in0.0020.9550.829-0.0010.003-0.008-0.001-0.003-0.0150.1160.284-0.0090.0260.0520.1250.1250.004-0.0071.0000.901-0.003-0.003
x_out0.0000.7990.9520.000-0.219-0.008-0.011-0.004-0.0170.1400.419-0.0100.0180.0370.0320.0320.003-0.0090.9011.0000.000-0.002
y_in-0.003-0.0030.0010.710-0.001-0.004-0.0020.015-0.0060.0050.0200.0040.0490.2250.1400.1400.006-0.006-0.0030.0001.0000.830
y_out-0.007-0.003-0.0010.8870.003-0.008-0.0020.013-0.009-0.0010.0160.0030.0260.1320.1700.1700.004-0.011-0.003-0.0020.8301.000

Missing values

2025-10-13T01:14:22.926861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-13T01:14:24.618114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

game_idplay_idplayer_to_predictnfl_idframe_idplay_directionabsolute_yardline_numberplayer_nameplayer_heightplayer_weightplayer_birth_dateplayer_positionplayer_sideplayer_rolex_iny_insadironum_frames_outputball_land_xball_land_yx_outy_out
02023090700101True461371right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.3220.690.310.4979.43267.682163.259998-0.2256.2217.28
12023090700101True461372right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.3520.660.360.74118.07268.662163.259998-0.2256.6316.88
22023090700101True461373right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.3920.630.440.76130.89269.782163.259998-0.2257.0616.46
32023090700101True461374right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.4320.610.480.62134.50269.782163.259998-0.2257.4816.02
42023090700101True461375right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.4820.580.540.44129.79269.062163.259998-0.2257.9115.56
52023090700101True461376right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.5820.630.610.2999.58274.002163.259998-0.2258.3415.10
62023090700101True461377right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.6520.620.690.4998.72274.902163.259998-0.2258.7514.57
72023090700101True461378right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.7320.600.871.0195.98277.782163.259998-0.2259.1414.01
82023090700101True461379right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.8220.590.990.9197.72279.152163.259998-0.2259.5113.41
92023090700101True4613710right42Justin Reid6-12041997-02-15SSDefenseDefensive Coverage51.9220.581.141.0198.11278.452163.259998-0.2259.8612.80
game_idplay_idplayer_to_predictnfl_idframe_idplay_directionabsolute_yardline_numberplayer_nameplayer_heightplayer_weightplayer_birth_dateplayer_positionplayer_sideplayer_rolex_iny_insadironum_frames_outputball_land_xball_land_yx_outy_out
56041620240107134018True524579left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver50.3018.803.154.74254.52291.741832.1399996.7132.7313.96
56041720240107134018True5245710left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver49.9418.703.804.66254.67291.741832.1399996.7132.2913.34
56041820240107134018True5245711left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver49.5318.604.344.17254.98294.911832.1399996.7131.9012.68
56041920240107134018True5245712left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver49.0518.484.924.00256.13295.911832.1399996.7131.5412.00
56042020240107134018True5245713left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver48.5418.385.353.41257.62294.221832.1399996.7131.2411.27
56042120240107134018True5245714left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver47.9718.265.823.06258.62288.711832.1399996.7130.9910.51
56042220240107134018True5245715left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver47.3718.156.222.68259.76288.711832.1399996.7130.789.73
56042320240107134018True5245716left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver46.7418.046.512.15260.65286.621832.1399996.7130.638.93
56042420240107134018True5245717left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver46.0717.946.802.03261.79285.471832.1399996.7130.528.12
56042520240107134018True5245718left50Chase Claypool6-42271998-07-07WROffenseTargeted Receiver45.3817.857.021.67263.04286.501832.1399996.7130.457.30